Papers with RLTA
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference (D19-1)
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| Challenge: | Recent research shows textual data alone may contain enough information about users' private-attributes that they do not want to disclose such as age, gender, location, political views and sexual orientation. |
| Approach: | They propose a novel Reinforcement Learning-based Text Anonymizor which extracts a latent representation of the original text w.r.t. a given task and leverages deep reinforcement learning to learn an optimal strategy for manipulating text representations w/ the received privacy and utility feedback. |
| Outcome: | The proposed approach preserves both privacy and utility of textual data while preserving its utility. |